Is creating dataset only by augmentation a bad practice?
Data augmentation is usually rotating, cropping and translating images. And this makes sense if your network could meet these kind of images.
If I take a digit recognition like LeNet, it is useless to complicate the task of the network by forcing it to learn rotated digits, which could lead to a more complex architecture and training and less accuracy in the task. Another example I could think of is human pose recognition (openpose project). As we humans usually stands with our feet on the bottom of the image and the head on top, openpose project didn't use rotation of images on the dataset.
So I would say data augmentation is a great tool (especially when we lack data), but I would only use it when the augmented data could be met when performing the task. If the digits are always oriented and placed on the middle of the image, it doesn't make much sense to use translations and rotations on the data set to augment it unless we really lack data.
In your example, it does make sense to me to create the dataset using data augmentation.